Video images are often corrupted by noise during the video image acquisition or transmission process. In order to improve the visual appearance of such images, an effective filtering technique is often required to reduce the noise level therein. Traditional noise reduction techniques mainly involve applying a linear filter such as an averaging filter to all of the pixels in a video frame (“image”). While this reduces noise level in the image, such a linear filtering technique also indiscriminately blurs edges in the image.
In order to prevent image edge blurring, a noise reduction filter must be adaptive to local structures, such as edges, in the image. One such adaptive technique is known as directional filtering. Directional filtering attempts to avoid image blurring by adapting linear filtering to image edge directions in such a way that the filter utilized is always applied along the edge direction not across the edge direction.
FIG. 1 shows a block diagram of an example directional filter 100. At each image pixel, first the 2-D local variance is computed by a local variance calculator 120 for a small window. Then, the 1-D local variances are computed along the horizontal, vertical, diagonal from upper left to lower right, and diagonal from upper right to lower left directions within the same window of pixels. To determine the edge direction, the 2-D variance is compared with a predetermined threshold in an edge direction detector block 140. If the 2-D variance is less than the threshold, then no edge is present at the pixel, and the pixel is considered having “no direction”. If the 2-D variance is greater than the threshold, then an edge is present at that pixel, and the direction with the smallest 1-D variance is considered as the edge direction of the pixel. Utilizing a filter 160, at “no direction” pixels, a 2-D average filter is applied. At a pixel with a detected edge direction, a 1-D average filter is applied along the detected direction. By filtering along image edge directions, the directional filter 100 is able to retain most of the image structures while reducing the noise level of the input image.
There are two major shortcomings to the directional filtering technique. The first is that the threshold value must be manually tuned and usually it is difficult to select the right value. An improperly selected threshold value will cause either image blurring or insufficient noise reduction. The second shortcoming of the directional filter is that the filter strength is fixed. That means a relatively clean image is processed the same way as a highly noisy image. This causes the relatively clean image to unnecessarily lose some fine structures and be degraded.